遇见数据集

RNA seq and MeRIP-seq of COV434 cells before and after FTO knockdown

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NIAID Data Ecosystem2026-03-13 收录
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Multifunctional N6-methyladenosine (m6A) has been revealed to be an important epigenetic component in various physiological and pathological processes, but its role in female ovarian aging remains unclear. Thus, we demonstrated m6A demethylase FTO downregulation and the ensuing increased m6A in granulosa cells (GCs) of human aged ovaries, while FTO-knockdown GCs showed faster aging-related phenotypes mediated. Using the m6A-RNA-sequence technique (m6A-seq), increased m6A was found in the FOS-mRNA-3'UTR, which is suggested to be an erasing target of FTO that slows the degradation of FOS-mRNA to upregulate FOS expression in GCs, eventually resulting in GC-mediated ovarian aging. FTO acts as a senescence-retarding protein via m6A, and FOS knockdown significantly alleviates the aging of FTO-knockdown GCs. Overall design: COV434 cells were transfected by negative control lentivirus(COV434-shNC) and Fto-knockdown lentivirus(COV434-shFTO) for 3 times. 6 simples were get by this way. RNA seq was done with half of each simples and MeRIP-seq(FTO antibodies were used) was done with another half of each simples.

多功能N6-甲基腺苷(N6-methyladenosine,m6A)已被证实为多种生理及病理过程中关键的表观遗传调控组分,但其在女性卵巢衰老中的作用仍未明确。为此,我们观察到人类衰老卵巢的颗粒细胞(granulosa cells,GCs)中m6A去甲基化酶FTO表达下调,伴随m6A水平升高;而敲低FTO的颗粒细胞会呈现出加速的衰老相关表型。我们采用m6A测序技术(m6A-RNA-sequence technique,m6A-seq)分析发现,FOS mRNA的3'非翻译区(3'UTR)存在m6A水平升高现象,该区域被证实为FTO的去甲基化靶点;FTO可通过消除该位点的m6A修饰以延缓FOS mRNA的降解,从而上调颗粒细胞中FOS的表达,最终介导颗粒细胞相关的卵巢衰老。FTO可通过m6A修饰发挥延缓衰老的作用,而敲低FOS可显著缓解敲低FTO的颗粒细胞的衰老表型。整体实验设计:将COV434细胞分别转染阴性对照慢病毒(COV434-shNC)与FTO敲低慢病毒(COV434-shFTO),实验重复3次,由此获得6份样本;每份样本取一半进行RNA测序,另一半则采用FTO抗体进行甲基化RNA免疫沉淀测序(MeRIP-seq)实验。

创建时间:
2021-10-19
搜集汇总
数据集介绍
RNA seq and MeRIP-seq of COV434 cells before and after FTO knockdown 数据集图片
背景与挑战
背景概述
该数据集包含COV434人类细胞系在FTO基因敲低前后进行的RNA免疫沉淀测序(RIP-Seq)数据,使用Illumina HiSeq 4000平台生成配对末端测序结果。数据涵盖FTO敲低(shFTO)和阴性对照(shNC)条件下的免疫沉淀(IP)及非IP样本,每个条件包含三个生物学重复,共12个实验,旨在研究FTO蛋白对RNA修饰的影响。
以上内容由遇见数据集搜集并总结生成
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